Conventional power-management schemes typically manage the power usage of a computing device by determining whether, and to what extent, a user is currently using the computing device. For example, during periods of inactivity, a power-management scheme may cause a computing device to reduce its power consumption. Conversely, when usage peaks, such schemes may increase the power consumption of the computing device to ensure maximum performance.
While conventional power-management schemes may provide the most optimal experience for users, these schemes fail to account for the impact of a device's power usage on a power grid. For example, when a high number of computing devices simultaneously draw power from a power grid, the collective draw of the computing devices may contribute to spikes on the power grid. Some researchers have even estimated that active computing devices (i.e., computing devices that are not in some form of sleep mode) within a single state may collectively draw as much as 322 Megawatts of power from a regional power grid during peak hours, which is the usage equivalent of approximately 200,000 households.
Unfortunately, if a high number of computing devices simultaneously draw power from a power grid, the power grid may experience unnecessary strain. This may require power-grid operators to impose rolling blackouts to reduce power draw and maintain power availability for a majority of customers.